A Sample Weighting and Score Aggregation Method for Multi-query Object Matching

In this paper, we propose a simple and effective method to properly assign weights to the query samples and compute aggregated matching scores using these weights in multi-query object matching. Multi-query object matching commonly exists in many real-life problems such as finding suspicious objects in surveillance videos. In this problem, a query object is represented by multiple samples and the matching candidates in a database are ranked according to their similarities to these query samples. In this context, query samples are not equally effective to find the target object in the database, thus one of the key challenges is how to measure the effectiveness of each query to find the correct matching object. So far, however, very little attention has been paid to address this issue. Therefore, we propose a simple but effective way, Inverse Model Frequency (IMF), to measure of matching effectiveness of query samples. Furthermore, we introduce a new score aggregation method to boost the object matching performance given multiple queries. We tested the proposed method for vehicle re-identification and image retrieval tasks. Our proposed approach achieves state-of-the-art matching accuracy on two vehicle re-identification datasets (VehicleID/VeRi-776) and two image retrieval datasets (the original & revisited Oxford/Paris). The proposed approach can seamlessly plug into many existing multi-query object matching approaches to further boost their performance with minimal effort.